arXiv subjects

Hao Sun

Publications and source records attributed to Hao Sun.

At least 505 records · Page 28Linked to original sources

arXiv2019

Degree of W-operator and Noncrossing Partition

Goulden and Jackson first introduced the cut-and-join operator. The cut-and-join is widely used in studying the Hurwitz number and many other topological recursion problems. Mironov, Morosov and Natanzon give a more general construction and call it W-operator W([n]). As a special case, the cut-and-join operator is W([2]). In this paper, we study the structure of W([n]). We prove that W([n]) can be written as the sum of n! terms and each term corresponds uniquely to a permutation in Sn. We also define the degree of each term. We prove that there is a correspondence between the terms of W([n]) with highest degree and the noncrossing partitions.

math.CO↗

arXiv2019

Double quarkonium hadroproduction as a probe of gluon Sivers function

The prediction of single spin asymmetry (SSA) in double $\rm J/ψ$ production in proton-proton collision is given out within the framework of non-relativistic QCD, using the recently obtained best fit parameters for the gluon Sivers function extracted from PHENIX data in $\rm p+p^\uparrow\toπ^0+X$. The color singlet state $\rm ^3 S_1^{(1)}$ and color octet state $\rm ^3 S_1^{(8)}$ are considered to the SSA contribution in the double $\rm J/ψ$ hadroproduction. Our result shows that a sizable asymmetry can be estimated as functions of different kinematic variables.

hep-ph↗

arXiv2019

Probing the Sivers asymmetries through $\rm J/ψ$ photoproduction in $\rm p^\uparrow p$ collision with forward proton tagging

In this paper we probe the Sivers asymmetries through $\rm J/ψ$ photoproduction in $\rm p^\uparrow p$ collision within the non-relativistic QCD framework, based on color octet model and the Transverse Momentum Dependent Parton Distributions (TMDs). Both the DGLAP evolution and the TMD evolution are included. The intensity and the sign of the Sivers asymmetry is strongly related on evolution model used to investigate the Gluon Sivers Function (GSF). A sizable asymmetry is obtained as a function of the rapidity, $\rm log{(x_γ)}$ or $\rm log{(x_{g})}$ dependence using a recent parametrization of GSF at the RHIC and AFTER@LHC experiments with the LHC planned forward detector acceptances.

hep-ph↗

arXiv2019

Photoproduction of $\rm J/ψ$ with forward hadron tagging in hadronic collisions

We study the photoproduction of $\rm J/ψ$ using the NRQCD formalism with forward hadron tagging at the Large Hadron Collider. We estimate the total cross sections and event rates with and without nuclear shadowing effects in high energy proton-proton, proton-nucleus and nucleus-nucleus collisions. Our results show that the processes which involve $\rm J/ψ$ photoproduction depend on the choice of forward detector acceptances $ξ$. Under some precise cut of $\rm p_{T}^{J/ψ}$ and $\rm z^{J/ψ}$ kinematic variables, we find that the distributions of photoproduction of $\rm J/ψ$ are led by the Fock state $\rm ^{1}S_{0}^{[8]}$ or $\rm ^{3}S_{1}^{[1]}$. The total cross sections and event rates will be smaller if the nuclear shadowing effects are considered. The processes give a crucial photoproduction signature at the LHC with forward detector acceptances. The exploration and the detection of the interactions will be useful for studying the mechanism of heavy quarkonium production.

hep-ph↗

arXiv2019

ResNet Can Be Pruned 60x: Introducing Network Purification and Unused Path Removal (P-RM) after Weight Pruning

The state-of-art DNN structures involve high computation and great demand for memory storage which pose intensive challenge on DNN framework resources. To mitigate the challenges, weight pruning techniques has been studied. However, high accuracy solution for extreme structured pruning that combines different types of structured sparsity still waiting for unraveling due to the extremely reduced weights in DNN networks. In this paper, we propose a DNN framework which combines two different types of structured weight pruning (filter and column prune) by incorporating alternating direction method of multipliers (ADMM) algorithm for better prune performance. We are the first to find non-optimality of ADMM process and unused weights in a structured pruned model, and further design an optimization framework which contains the first proposed Network Purification and Unused Path Removal algorithms which are dedicated to post-processing an structured pruned model after ADMM steps. Some high lights shows we achieve 232x compression on LeNet-5, 60x compression on ResNet-18 CIFAR-10 and over 5x compression on AlexNet. We share our models at anonymous link http://bit.ly/2VJ5ktv.

cs.LG↗

arXiv2019

Computer-aided diagnosis in histopathological images of the endometrium using a convolutional neural network and attention mechanisms

Uterine cancer, also known as endometrial cancer, can seriously affect the female reproductive organs, and histopathological image analysis is the gold standard for diagnosing endometrial cancer. However, due to the limited capability of modeling the complicated relationships between histopathological images and their interpretations, these computer-aided diagnosis (CADx) approaches based on traditional machine learning algorithms often failed to achieve satisfying results. In this study, we developed a CADx approach using a convolutional neural network (CNN) and attention mechanisms, called HIENet. Because HIENet used the attention mechanisms and feature map visualization techniques, it can provide pathologists better interpretability of diagnoses by highlighting the histopathological correlations of local (pixel-level) image features to morphological characteristics of endometrial tissue. In the ten-fold cross-validation process, the CADx approach, HIENet, achieved a 76.91 $\pm$ 1.17% (mean $\pm$ s. d.) classification accuracy for four classes of endometrial tissue, namely normal endometrium, endometrial polyp, endometrial hyperplasia, and endometrial adenocarcinoma. Also, HIENet achieved an area-under-the-curve (AUC) of 0.9579 $\pm$ 0.0103 with an 81.04 $\pm$ 3.87% sensitivity and 94.78 $\pm$ 0.87% specificity in a binary classification task that detected endometrioid adenocarcinoma (Malignant). Besides, in the external validation process, HIENet achieved an 84.50% accuracy in the four-class classification task, and it achieved an AUC of 0.9829 with a 77.97% (95% CI, 65.27%-87.71%) sensitivity and 100% (95% CI, 97.42%-100.00%) specificity. In summary, the proposed CADx approach, HIENet, outperformed three human experts and four end-to-end CNN-based classifiers on this small-scale dataset composed of 3,500 hematoxylin and eosin (H&E) images regarding overall classification performance.

cs.CV↗

arXiv2019

Learning Fast Matching Models from Weak Annotations

This paper proposes a novel training scheme for fast matching models in Search Ads, which is motivated by the real challenges in model training. The first challenge stems from the pursuit of high throughput, which prohibits the deployment of inseparable architectures, and hence greatly limits the model accuracy. The second problem arises from the heavy dependency on human provided labels, which are expensive and time-consuming to collect, yet how to leverage unlabeled search log data is rarely studied. The proposed training framework targets on mitigating both issues, by treating the stronger but undeployable models as annotators, and learning a deployable model from both human provided relevance labels and weakly annotated search log data. Specifically, we first construct multiple auxiliary tasks from the enumerated relevance labels, and train the annotators by jointly learning from those related tasks. The annotation models are then used to assign scores to both labeled and unlabeled training samples. The deployable model is firstly learnt on the scored unlabeled data, and then fine-tuned on scored labeled data, by leveraging both labels and scores via minimizing the proposed label-aware weighted loss. According to our experiments, compared with the baseline that directly learns from relevance labels, training by the proposed framework outperforms it by a large margin, and improves data efficiency substantially by dispensing with 80% labeled samples. The proposed framework allows us to improve the fast matching model by learning from stronger annotators while keeping its architecture unchanged. Meanwhile, our training framework offers a principled manner to leverage search log data in the training phase, which could effectively alleviate our dependency on human provided labels.

cs.IR↗

arXiv2019

Doppler Fluctuation Spectroscopy of Intracellular Dynamics in Living Tissue

Intracellular dynamics in living tissue are dominated by active transport driven by bioenergetic processes far from thermal equilibrium. Intracellular constituents typically execute persistent walks. In the limit of long mean-free paths, the persistent walks are ballistic, exhibiting a "Doppler edge" in light scattering fluctuation spectra. At shorter transport lengths, the fluctuations are described by lifetime-broadened Doppler spectra. Dynamic light scattering from transport in the ballistic, diffusive or the cross-over regime is derived analytically, including the derivation of autocorrelation functions through a driven damped harmonic oscillator analog for light scattering from persistent walks. The theory is validated through Monte Carlo simulations. Experimental evidence for the Doppler edge in 3D living tissue is obtained using biodynamic imaging based on low-coherence interferometry and digital holography.

q-bio.QM↗

arXiv2019

Deformation of Locally Free Sheaves and Hitchin Pairs over Nodal Curve

In this article, we study the deformation theory of locally free sheaves and Hitchin pairs over a nodal curve. As a special case, the infinitesimal deformation of these objects gives the tangent space of the corresponding moduli spaces, which can be used to calculate the dimension of the corresponding moduli space. We show that the deformation of locally free sheaves and Hitchin pairs over a nodal curve is equivalent to the deformation of generalized parabolic bundles and generalized parabolic Hitchin pairs over the normalization of the nodal curve respectively.

math.AG↗

arXiv2019

Long Short-Term Memory Spatial Transformer Network

Spatial transformer network has been used in a layered form in conjunction with a convolutional network to enable the model to transform data spatially. In this paper, we propose a combined spatial transformer network (STN) and a Long Short-Term Memory network (LSTM) to classify digits in sequences formed by MINST elements. This LSTM-STN model has a top-down attention mechanism profit from LSTM layer, so that the STN layer can perform short-term independent elements for the statement in the process of spatial transformation, thus avoiding the distortion that may be caused when the entire sequence is spatially transformed. It also avoids the influence of this distortion on the subsequent classification process using convolutional neural networks and achieves a single digit error of 1.6\% compared with 2.2\% of Convolutional Neural Network with STN layer.

eess.IV↗

arXiv2018

Inequalities of Chern classes on nonsingular projective $n$-folds of Fano and general type with ample canonical bundle

Let $X$ be a nonsingular projective $n$-fold $(n\ge 2)$ of Fano or of general type with ample canonical bundle $K_X$ over an algebraic closed field $κ$ of any characteristic. We produce a new method to give a bunch of inequalities in terms of all the Chern classes $c_1, c_2, \cdots, c_n$ by pulling back Schubert classes in the Chow group of Grassmannian under the Gauss map. Moreover, we show that if the characteristic of $κ$ is $0$, then the Chern ratios $(\frac{c_{2,1^{n-2}}}{c_{1^n}}, \frac{c_{2,2,1^{n-4}}}{c_{1^n}}, \cdots, \frac{c_{n}}{c_{1^n}})$ are contained in a convex polyhedron for all $X$. So we give an affirmative answer to a generalized open question, that whether the region described by the Chern ratios is bounded, posted by Hunt (\cite{Hun}) to all dimensions. As a corollary, we can get that there exist constants $d_1$, $d_2$, $d_3$ and $d_4$ depending only on $n$ such that $d_1K_X^n\leχ_{top}(X)\le d_2 K_X^n$ and $d_3K_X^n\leχ(X, \mathscr{O}_X)\le d_4 K_X^n$. If the characteristic of $κ$ is positive, $K_X$ (or $-K_X$) is ample and $\mathscr{O}_X(K_X)$ ($\mathscr{O}_X(-K_X)$, respectively) is globally generated, then the same results hold.

math.AG↗

arXiv2018

Excitonic Complexes and Optical Gain in Two-Dimensional Molybdenum Ditelluride Well below Mott Transition

Strong Coulomb interaction in 2D materials provides unprecedented opportunities for studying many key issues of condensed matter physics, such as co-existence and mutual conversions of excitonic complexes, fundamental optical processes associated with their conversions, and their roles in the celebrated Mott transition. Recent lasing demonstrations in 2D materials raise important questions about the existence and origin of optical gain and possible roles of excitonic complexes. While lasing occurred at extremely low densities dominated by various excitonic complexes, optical gain was observed in the only experiment at densities several orders of magnitude higher, exceeding the Mott density. Here, we report a new gain mechanism involving charged excitons or trions well below the Mott density in 2D molybdenum ditelluride. Our combined experimental and modeling study not only reveals the complex interplays of excitonic complexes well below the Mott transition, but also provides foundation for lasing at extremely low excitation levels, important for future energy efficient photonic devices.

cond-mat.mes-hall↗

arXiv2018

Position Detection and Direction Prediction for Arbitrary-Oriented Ships via Multitask Rotation Region Convolutional Neural Network

Ship detection is of great importance and full of challenges in the field of remote sensing. The complexity of application scenarios, the redundancy of detection region, and the difficulty of dense ship detection are all the main obstacles that limit the successful operation of traditional methods in ship detection. In this paper, we propose a brand new detection model based on multitask rotational region convolutional neural network to solve the problems above. This model is mainly consist of five consecutive parts: Dense Feature Pyramid Network (DFPN), adaptive region of interest (ROI) Align, rotational bounding box regression, prow direction prediction and rotational nonmaximum suppression (R-NMS). First of all, the low-level location information and high-level semantic information are fully utilized through multiscale feature networks. Then, we design Adaptive ROI Align to obtain high quality proposals which remain complete spatial and semantic information. Unlike most previous approaches, the prediction obtained by our method is the minimum bounding rectangle of the object with less redundant regions. Therefore, rotational region detection framework is more suitable to detect the dense object than traditional detection model. Additionally, we can find the berthing and sailing direction of ship through prediction. A detailed evaluation based on SRSS for rotation detection shows that our detection method has a competitive performance.

cs.CV↗

arXiv2018

$H_5^{\pm\pm}h^0$ production via vector-boson fusion in Georgi-Machacek model at hadron colliders

The Georgi-Machacek (GM) model is a distinctive TeV-scale extension of the Standard Model (SM) due to the introduction of two (one real and one complex) scalar triplets to the Higgs sector. It predicts the existence of doubly charged Higgs bosons $H_5^{\pm\pm}$ and the vacuum expectation value of Higgs triplets $v_Δ$ can reach a few tens of GeV. In this paper, we perform a parameter scan of the GM model within the H5plane benchmark scenario and investigate in detail the single production of a doubly charged Higgs boson in association with a SM-like Higgs boson via vector-boson fusion at the $14~ {\rm TeV}$ LHC and $70~ {\rm TeV}$ Super Proton-Proton Collider. Both integrated cross section and differential distributions with respect to some kinematic variables for $pp \rightarrow W^{\pm}W^{\pm} \rightarrow H_5^{\pm\pm} h^0 + 2\, {\rm jets}$ are provided up to the QCD next-to-leading order. In the signal-background analysis we employ the {\sc madspin} method to take into account the spin correlation and finite width effects of the intermediate Higgs and $W$ bosons and present some kinematic distributions of final leptons. The numerical results show that the SM background can be suppressed remarkably and the $H_5^{\pm\pm} h^0$ vector-boson fusion signal can be directly detected at future high-luminosity, high-energy hadron colliders by imposing a proper cut on the transverse mass $M_{T, \ell_1 \ell_2}$.

hep-ph↗

arXiv2018

Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks

Ship detection has been playing a significant role in the field of remote sensing for a long time but it is still full of challenges. The main limitations of traditional ship detection methods usually lie in the complexity of application scenarios, the difficulty of intensive object detection and the redundancy of detection region. In order to solve such problems above, we propose a framework called Rotation Dense Feature Pyramid Networks (R-DFPN) which can effectively detect ship in different scenes including ocean and port. Specifically, we put forward the Dense Feature Pyramid Network (DFPN), which is aimed at solving the problem resulted from the narrow width of the ship. Compared with previous multi-scale detectors such as Feature Pyramid Network (FPN), DFPN builds the high-level semantic feature-maps for all scales by means of dense connections, through which enhances the feature propagation and encourages the feature reuse. Additionally, in the case of ship rotation and dense arrangement, we design a rotation anchor strategy to predict the minimum circumscribed rectangle of the object so as to reduce the redundant detection region and improve the recall. Furthermore, we also propose multi-scale ROI Align for the purpose of maintaining the completeness of semantic and spatial information. Experiments based on remote sensing images from Google Earth for ship detection show that our detection method based on R-DFPN representation has a state-of-the-art performance.

cs.CV↗

arXiv2018

Attention Incorporate Network: A network can adapt various data size

In traditional neural networks for image processing, the inputs of the neural networks should be the same size such as 224*224*3. But how can we train the neural net model with different input size? A common way to do is image deformation which accompany a problem of information loss (e.g. image crop or wrap). Sequence model(RNN, LSTM, etc.) can accept different size of input like text and audio. But one disadvantage for sequence model is that the previous information will become more fragmentary during the transfer in time step, it will make the network hard to train especially for long sequential data. In this paper we propose a new network structure called Attention Incorporate Network(AIN). It solve the problem of different size of inputs including: images, text, audio, and extract the key features of the inputs by attention mechanism, pay different attention depends on the importance of the features not rely on the data size. Experimentally, AIN achieve a higher accuracy, better convergence comparing to the same size of other network structure

cs.CV↗

arXiv2018

Progressive Neural Networks for Image Classification

The inference structures and computational complexity of existing deep neural networks, once trained, are fixed and remain the same for all test images. However, in practice, it is highly desirable to establish a progressive structure for deep neural networks which is able to adapt its inference process and complexity for images with different visual recognition complexity. In this work, we develop a multi-stage progressive structure with integrated confidence analysis and decision policy learning for deep neural networks. This new framework consists of a set of network units to be activated in a sequential manner with progressively increased complexity and visual recognition power. Our extensive experimental results on the CIFAR-10 and ImageNet datasets demonstrate that the proposed progressive deep neural network is able to obtain more than 10 fold complexity scalability while achieving the state-of-the-art performance using a single network model satisfying different complexity-accuracy requirements.

cs.CV↗

arXiv2018

Squeezed back-to-back correlation of $\bqD^0{\bar \bqD}^0$ in relativistic heavy-ion collisions

We investigate the squeezed back-to-back correlation (BBC) of $D^0\!{\bar D}^0$ in relativistic heavy-ion collisions, using the in-medium mass modification calculated with a self-energy in hot pion gas and the source space-time distributions provided by the viscous hydrodynamic code VISH2+1. It is found that the BBC of $D^0\!{\bar D}^0$ is significant in peripheral Au+Au collisions at the RHIC energy. A possible way to detect the BBC in experiment is presented.

nucl-th↗